학술논문

Short-Term Traffic Speed Prediction for an Urban Corridor.
Document Type
Article
Source
Computer-Aided Civil & Infrastructure Engineering. Feb2017, Vol. 32 Issue 2, p154-169. 16p.
Subject
*TRAFFIC speed
*INTELLIGENT transportation systems
*GLOBAL Positioning System
*TRAFFIC engineering
*SUPPORT vector machines
Language
ISSN
1093-9687
Abstract
Short-term traffic speed prediction is one of the most critical components of an intelligent transportation system (ITS). The accurate and real-time prediction of traffic speeds can support travellers' route choices and traffic guidance/control. In this article, a support vector machine model (single-step prediction model) composed of spatial and temporal parameters is proposed. Furthermore, a short-term traffic speed prediction model is developed based on the single-step prediction model. To test the accuracy of the proposed short-term traffic speed prediction model, its application is illustrated using GPS data from taxis in Foshan city, China. The results indicate that the error of the short-term traffic speed prediction varies from 3.31% to 15.35%. The support vector machine model with spatial-temporal parameters exhibits good performance compared with an artificial neural network, a k-nearest neighbor model, a historical data-based model, and a moving average data-based model. [ABSTRACT FROM AUTHOR]